Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Skin Disease Detection: Machine Learning vs Deep Learning

Version 1 : Received: 10 September 2021 / Approved: 13 September 2021 / Online: 13 September 2021 (11:54:04 CEST)

How to cite: Bandyopadhyay, S.; Bhaumik, A.; Poddar, S. Skin Disease Detection: Machine Learning vs Deep Learning. Preprints 2021, 2021090209 (doi: 10.20944/preprints202109.0209.v1). Bandyopadhyay, S.; Bhaumik, A.; Poddar, S. Skin Disease Detection: Machine Learning vs Deep Learning. Preprints 2021, 2021090209 (doi: 10.20944/preprints202109.0209.v1).

Abstract

Skin disease is a very common disease for humans. In the medical industry detecting skin disease and recognizing its type is a very challenging task. Due to the complexity of human skin texture and the visual closeness effect of the diseases, sometimes it is really difficult to detect the exact type. Therefore, it is necessary to detect and recognize the skin disease at its very first observation. In today's era, artificial intelligence (AI) is rapidly growing in medical fields. Different machine learning (ML) and deep learning(DL) algorithms are used for diagnostic purposes. These methods drastically improve the diagnosis process and also speed up the process. In this paper, a brief comparison between the machine learning process and the deep learning process was discussed. In both processes, three different and popular algorithms are used. For the machine Learning process Bagged Tree Ensemble, K-Nearest Neighbor (KNN), and Support Vector Machine(SVM) algorithms were used. For the deep learning process three pre-trained deep neural network models

Keywords

Skin Disease Detection; Machine Learning (ML); Deep Learning(DL); Artificial Intelligence

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